Thin polished plates such as silicon wafers and the like are a very important part of modern technology. A wafer, for instance, may refer to a thin slice of semiconductor material used in the fabrication of integrated circuits and other devices. Other examples of thin polished plates may include magnetic disc substrates, gauge blocks and the like. While the technique described here refers mainly to wafers, it is to be understood that the technique also is applicable to other types of polished plates as well. The term wafer and the term thin polished plate may be used interchangeably in the present disclosure.
Wafers are subject to defect inspections. Defects can be random or systematic. Systematic defects may occur on certain design patterns and may be referred to as hotspots (i.e., weak patterns, or patterns produced on a wafer that deviated from the design). One of the objectives of defect inspection is to detect and quantify these hotspots. For instance, an inspection process may utilize an inspection tool to scan a wafer and bin the detected defects using design-based grouping (DBG). The defects may be sampled and reviewed using a scanning electron microscope (SEM) and manually classified to determine the presence of hotspots. Simulations may be utilized to help predict patterns or sites that are susceptible to failures, which may in turn be used to help users place inspection care in areas that may contain such patterns or sites.
It is noted that while the inspection process described above can be helpful, this inspection process is also associated with some disadvantages. For example, this inspection process relies on human eyes to determine the presence of defects, which means this inspection process is only capable of processing small sample sizes and lacks validation. In practice, systematic defects that are detected with critical dimension (CD) changes of about 20-30% may be discarded. In addition, since there is no automation and all classification must be done by a human, the sample size is typically limited to no more than about 5 thousand defects and is prone to error due to sampling and fatigue. With the explosion of data volume resulting from increased detection sensitivity, effectiveness of sampling can be limited unless an automated approach is developed. Furthermore, simulations help predict risky sites but they do not help user to identify if the sites actually fail post processing.
Therein lies a need for providing methods and systems for weak pattern detection and quantification without the aforementioned shortcomings.